1,571 research outputs found

    Local Government and Local Development: Bridging the Gap through Critical Discourse: Evidence from the Commonwealth Caribbean

    Get PDF
    Local development, whether construed broadly as community development or more narrowly as local as economic development (LED) is not always associated with local government but rather is the purview of a central government department or agency in Anglophone Caribbean policy systems. However with the emergence of ‘local place - and people-oriented approaches’ to development that offer new propositions about how to respond to risks and opportunities brought by globalization, local government is seen increasingly as an appropriate institutional context in which to pursue short-range objectives, such as creation of market opportunities and redressing the disparities within national economies; as well as the long-range goal of social transformation. A developmental role for local government raises two questions that form the central concerns of this paper: What are the institutional and organisational imperatives of a developmental role for local government? To what extent have these imperatives been addressed in reform? A critical analysis of local government reform policies in Trinidad and Tobago and Jamaica revealed substantive convergence around local development as an outcome of reform but also important divergence in the approach to achieving this goal which suggests the absence of a cohesive model. The paper argues for a new agenda in reform that links local government more consistently with a local development strategy. It asserts that such a strategy must incorporate gender equality, the informal economy and institutional organisational capacity in the process of transformation and as a basis for creating a local context in which all types of resources can be maximized in the process of wealth creation in a locality

    Algorithms for bundling and pricing trucking services: Deterministic and stochastic approaches

    Get PDF
    Bundling and pricing trucking services is an important strategic decision for carriers. This is helpful when they consider the incorporation of new businesses to their networks, look for economic and optimal operations, and develop revenue management strategies. Reverse combinatorial auctions for trucking services are real-world examples that illustrate the necessity of such strategies. In these auctions, a shipper asks carriers for quotes to serve combinations of lanes and the carriers have to bundle demand and price it properly. This dissertation explores several dimensions of the problem employing state-of-the-art analytical tools. These dimensions include: Truckload (TL) and less-than-truckload (LTL) operations, behavioral attributes driving the selection of trucking services, and consideration of deterministic and stochastic demand. Analytical tools include: advanced econometrics, network modeling, statistical network analysis, combinatorial optimization, and stochastic optimization. The dissertation is organized as follows. Chapter 1 introduces the problem and related concepts. Chapter 2 studies the attributes driving the selection of trucking services and proposes an econometric model to quantify the shipper willingness to pay using data from a discrete choice experiment. Chapter 3 proposes an algorithm for demand clustering in freight logistics networks using historical data from TL carriers. Chapter 4 develops an algorithmic approach for pricing and demand segmentation of bundles in TL combinatorial auctions. Chapter 5 expands the latter framework to consider stochastic demand. Chapter 6 uses an analytical approach to demonstrate the benefits of in-vehicle consolidation for LTL carriers. Finally, Chapter 7 proposes an algorithm for pricing and demand segmentation of bundles in LTL combinatorial auctions that accounts for stochastic demand. This research provides meaningful negotiation guidance for shippers and carriers, which is supported by quantitative methods. Likewise, numerical experiments demonstrate the benefits and efficiencies of the proposed algorithms, which are transportation modeling contributions

    Building localised interactions between universities and cities through university spatial development

    Get PDF
    Universities are important players in the global development of the knowledge economy, alongside being significant contributors to the economic development of their host cities. They are both significant knowledge enterprises, as well as the suppliers of the human and intellectual capital on which the knowledge-based economy depends. What seems under-explored is how deliberative partnerships between universities and city authorities can develop around projects of mutual benefit, especially based on campus development. In this paper, with the help of five case studies (QUT, MIT, Harvard, Twente and Newcastle universities), we investigate how the spatial development of universities can be one of the main meeting points between the city and university, and how it can be used for stimulating economic development and managing growth. These cases show that university-city collaborative initiatives focused on university properties represent a desire to produce creative and competitive new urban spaces which reinforce the position of the university and the city in global economy. They also show that these developments need to be jointly managed to avoid undesirable impacts on either side

    STRATEGIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS UNDER MORE REALISTIC CONDITIONS

    Get PDF
    Emergency medical service (EMS) systems provide medical care to pre-hospital patients who need rapid response and transportation. This dissertation proposes a new realistic approach for EMS systems in two major focuses: multiple unit dispatching and relocation strategies. This work makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the expected survival rate. Simulation models are proposed to determine the optimization. A heuristic algorithm is developed for large-scale problems. Numerical results show that dispatching while considering call priorities, rather than always dispatching the closest medical units, could improve the effectiveness of EMS systems. Additionally, we extend the model of multiple-unit dispatch to examine fairness between call priorities. We consider the potentially-life-threatening calls which could be upgraded to life-threatening. We formulate the fairness problem as an integer programming model solved using simulation optimization. Taking into account fairness between priorities improves the performance of EMS systems while still operating at high efficiency. As another focus, we consider dynamic relocation strategy using a nested-compliance table policy. For each state of the EMS systems, a decision must be made regarding exactly which ambulances will be allocated to which stations. We determine the optimal nested-compliance table in order to maximize the expected coverage, in the binary sense, as will be later discussed. We formulate the nested-compliance table model as an integer program, for which we approximate the steady-state probabilities of EMS system to use as parameters to our model. Simulation is used to investigate the performance of the model and to compare the results to a static policy based on the adjusted maximum expected covering location problem (AMEXCLP). Additionally, we extend the nested-compliance table model to consider an upper bound on relocation time. We analyze the decision regarding how to partition the service area into smaller sub-areas (districts) in which each sub-area operates independently under separate relocation strategies. We embed the nested-compliance table model into a tabu search heuristic algorithm. Iteration is used to search for a near-optimal solution. The performance of the tabu search heuristic and AMEXCLP are compared in terms of the realized expected coverage of EMS systems

    Optimal Parking Planning for Shared Autonomous Vehicles

    Full text link
    Parking is a crucial element of the driving experience in urban transportation systems. Especially in the coming era of Shared Autonomous Vehicles (SAVs), parking operations in urban transportation networks will inevitably change. Parking stations will serve as storage places for unused vehicles and depots that control the level-of-service of SAVs. This study presents an Analytical Parking Planning Model (APPM) for the SAV environment to provide broader insights into parking planning decisions. Two specific planning scenarios are considered for the APPM: (i) Single-zone APPM (S-APPM), which considers the target area as a single homogeneous zone, and (ii) Two-zone APPM (T-APPM), which considers the target area as two different zones, such as city center and suburban area. S-APPM offers a closed-form solution to find the optimal density of parking stations and parking spaces and the optimal number of SAV fleets, which is beneficial for understanding the explicit relationship between planning decisions and the given environments, including demand density and cost factors. In addition, to incorporate different macroscopic characteristics across two zones, T-APPM accounts for inter- and intra-zonal passenger trips and the relocation of vehicles. We conduct a case study to demonstrate the proposed method with the actual data collected in Seoul Metropolitan Area, South Korea. Sensitivity analyses with respect to cost factors are performed to provide decision-makers with further insights. Also, we find that the optimal densities of parking stations and spaces in the target area are much lower than the current situations.Comment: 27 pages, 9 figures, 9 table
    • …
    corecore